When using matplot to plot a matrix using:
matplot(t, X[,1:4], col=1:4, lty = 1, xlab="Time", ylab="Stock Value")
my graph comes out as:
How do I reduce the line thickness? I previously used a different method and my graph was fine:
I have tried manupilating lwd but to no avail.
Even tried plot(t, X[1:4097,1]), yet the line being printed is very thick. Something wrong with my R?
EDIT: Here is the code I used to produce the matrix X:
####Inputs mean return, volatility, time period and time step
mu=0.25; sigma=2; T=1; n=2^(12); X0=5;
#############Generating trajectories for stocks
##NOTE: Seed is fixed. Changing seed will produce
##different trajectories
dt=T/n
t=seq(0,T,by=dt)
set.seed(201)
X <- matrix(nrow = n+1, ncol = 4)
for(i in 1:4){
X[,i] <- c(X0,mu*dt+sigma*sqrt(dt)*rnorm(n,mean=0,sd=1))
X[,i] <- cumsum(X[,i])
}
colnames(X) <- paste0("Stock", seq_len(ncol(X)))
Just needed to add type = "l" to matplot(....). Plots fine now.
matplot(t, X[,1:4], col=1:4, type = "l", xlab="Time", ylab="Stock Value")
Related
I am trying to plot 18 individual plots on a 3x6 multiplot in R. To be more efficient I have created these plots as a loop, however I would like the plots in each column to have their own color (i.e. the all the plots in column 1 would be red, all the plots in column 2 would be blue etc.). Is there a way I can do this while still retaining loop format?
par(mfcol = c(3,6))
for(i in 1:6)
{
plot(sigma_trace[,i], type ='l', main = paste("Sigma Traceplot Chain", i))
plot(theta_1_trace[,i], type = 'l', main = paste("Theta[1] Traceplot Chain", i))
plot(theta_2_trace[,i], type = 'l', main = paste("Theta[2] Traceplot Chain", i))
}
So basically, I think I want each loop statement to follow the same pattern of colours. Is this possible?
Thanks.
You can make a colour palette using RColorBrewer and then call each colour in your loop. For example.
library(RColorBrewer)
# set the colour palette
cols <- brewer.pal(4,'Set2')
# variables to plot
x = (1:250)/10
y = cos(x)
# plot in the loop
op <- par(mfrow = c(2, 2))
for (i in 1:4){
plot(x, y, col=cols[i], type='l', lwd=3)
}
par(op)
Here's an overview of the package.
In Base R you can use colorRampPalette() to create gradient, or you can even just make an object with the colours that you wan to reference:
plotcolors <- colorRampPalette(c("gold","blue"))(6)
par(mfrow = c(2, 3))
for(i in 1:6){
plot(1:10,1:10,type='l',col=plotcolors[i])
}
If you want to specify all 6 of your colours its as easy as modifying the above code
plotcolors <- c("red","blue","green","black","yellow","purple")
I want to create a function, that result will be a plot of moniomals ( degree less than "n").
I wrote the simple code.
Monomial=function(m){
x=1:100
y=1:100
for(i in m) x2=x^m
plot(y,x2,type="l",col="red",xlab="Arguments",ylab="Values",
main=expression("Monomials"))
But for example: Monomial(3) I getting plot x^3. I need yet x^1 and x^2. How to name each line?
Here is what you need:
Monomial <- function(m){
x <- 1:100
cols <- palette(rainbow(m))
plot(x,x,type="l",col = cols[1],xlab="Arguments",ylab="Values",
main=expression("Monomials"))
for (d in 2:m){
lines(x, x^d, type="l", col=cols[d])
}
legend(90, 60, legend=c(as.character(paste0("x",1:m))),
col=cols, lty=1, cex=0.6)
}
You need to generate colors. This is what the cols variable achieves. lines adds a new curve to existing axes. Finally, ledend adds a legend to the plot.
I have a question, I have several variables (apriori I don't know how many). And I want to plot them and add a legend for all of them automatically. I did this, but the problem is that I can't understand how to add expression here. I want that the legend looks like $b_1$ and not beta[1].
I have a matrix: beta_tilde. And I plot it:
n <- 5
beta_tilde <- matrix(rnorm(100*n), nr = 100)
ts.plot(beta_tilde, ylim = c(min(beta_tilde), max(beta_tilde)), col = 1:n)
legend("topright" , legend = eval(substitute(paste("beta[", v,"]", sep = ""), list(v=1:n))), col=1:n, lty=1, cex=0.7)
substitute doesn't return an expression vector of length greater 1. Your approach with eval and paste is misguided anyway. You should study some examples of computing on the language.
I find a for loop easiest here:
leg <- vector("expression", n)
for (i in seq_len(n)) leg[[i]] <- substitute(beta[v], list(v = i))
legend("topright", legend = leg, col=1:n, lty=1, cex=0.7)
If I plot a data and use lines to superimpose the same data points on the graph, I get the same data points. Lets say
x<-rnorm(100)
plot(x, type="p")
lines(x, type="p",pch=2)
However, I have realized that there is a distortion in R plots when the same is done in a multipanel graph. It seems R is unable to recall the exact values on the y-axis when you plot the same data again. A simple code below shows the outputs from "plot" and "lines" are not the same.
set.seed(1000)
Range<-rbind(rep(0,4),c(100,100,1,100));thres<-70
Ylab<-c("MAD","Bias","CP","CIL")
X<-list(EVI=cbind(runif(10,0,100),runif(10,0,100),
runif(10,0,1),runif(10,0,100)),
Qp=cbind(runif(10,0,100),runif(10,0,100),runif(10,0,1),runif(10,0,100)))
Plot<-function(x,Pch=1,thres)
{
par(mfrow=c(1,4),las=2)
for(j in 1:4)
{
plot(x[,j],xaxt = "n",xlab="Estimator",
ylab=Ylab[j],type = "p", pch = Pch, ylim=Range[,j])
par(mfg=c(1,j))
axis(1, at=1:nrow(x), labels=LETTERS[1:nrow(x)])
if(j!=3){
par(mfg=c(1,j))
abline(h=thres,col=2)
}else{
par(mfg=c(1,j))
abline(h=c(0.90,0.95,0.99),lty=c(2,1,2),col=rep(2,3))
}
}
}
Line<-function(x,Pch)
{
for(j in 1:ncol(x)) {
par(mfg=c(1,j))
lines(x[,j], type = "p", pch = Pch,col=2)
}
}
lapply(X,function(dat)Plot(dat,thres=thres))
## First panel
Line(X$EVI,Pch=2)
## Move to second panel
Line(X$Qp,Pch=2)
What explains the distortions in the positioning of the points in the 3rd column? Note that, I have included the range of each data courtesy #WhiteViking in the "Plot" function. However, the distortion keeps showing. Thank you
The problem is in the ordering of 'plot' and 'lines'.
Code like this, with all 3 'plot' commands upfront:
set.seed(1)
X <- cbind(rnorm(20), 2 * rnorm(20), 3 * rnorm(20))
par(mfrow = c(1,3))
for (i in 1:3) {
plot(X[,i])
}
for (i in 1:3) {
par(mfg = c(1,i))
lines(X[,i], type = "p", col = 2, pch = 3)
}
yields misaligment:
In the example above the first 'lines' command that get executed bases its scaling on the last 'plot' that happened. Since that had a larger vertical range than the first, the scaling of the 'lines' is incorrect.
Whereas structured like so:
set.seed(1)
X <- cbind(rnorm(20), 2 * rnorm(20), 3 * rnorm(20))
par(mfrow = c(1,3))
for (i in 1:3) {
par(mfg = c(1,i))
plot(X[,i])
lines(X[,i], type = "p", col = 2, pch = 3)
}
it gives correct alignment of 'plot' and 'lines':
You'll probably have to rework your code to group 'plot' and 'lines' together for each sub-plot.
When the third column is converted to percentages, the ylim becomes uniform and hence there isn't such distortion. However, it will be good to get a way around it instead of such adhoc transformation
plot() sets up a coordinate system via plot.window based on the range of the data. This information is apparently stored in par(usr) for the latest plot, which means that if you want to revisit older plots, you should store those usr values and reset them accordingly,
set.seed(123)
d1 <- data.frame(x=1:10, y=rnorm(10))
d2 <- data.frame(x=1:10, y=10*rnorm(10))
par(mfrow=c(1,2),mar=c(2.5,2.5,0,0))
plot(d1, type="p")
usr1 <- par("usr")
plot(d2, type="p")
usr2 <- par("usr")
par(mfg=c(1,1), usr=usr1)
points(d1, col="red", pch=3)
par(mfg=c(1,2), usr=usr2)
points(d2, col="red", pch=3)
So I am trying to plot two p values from two different data frames and compare them to the normal distribution in QQplot in R
here is the code that I am using
## Taking values from 1st dataframe to plot
Rlogp = -log10(trialR$PVAL)
Rindex <- seq(1, nrow(trialR))
Runi <- Rindex/nrow(trialR)
Rloguni <- -log10(Runi)
## Taking values from 2nd dataframe to plot on existing plot
Nlogp = -log10(trialN$PVAL)
Nlogp = sort(Nlogp)
Nindex <- seq(1, nrow(trialN))
Nuni <- Nindex/nrow(trialN)
Nloguni <- -log10(Nuni)
Nloguni <- sort(Nloguni)
qqplot(Rloguni, Rlogp, xlim=range(0,6), ylim=range(0,6), col=rgb(100,0,0,50,maxColorValue=255), pch=19, lwd=2, bty="l",xlab ="", ylab ="")
qqline(Rloguni, Rlogp,distribution=qnorm, lty="dashed")
par(new=TRUE, cex.main=4.8, col.axis="white")
plot(Nloguni, Nlogp, xlim=range(0,6), ylim=range(0,6), col=rgb(0,0,100,50,maxColorValue=255), pch=19, lwd=2, bty="l",xlab ="", ylab ="")
The code plot the graph effectively,but I am not sure of the qqline as it seems bit offset... Can someone tell me if I am doing the correct way or is there something to change
the TARGET plot will look something like this - without the third data value..